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ARS Home » Midwest Area » Peoria, Illinois » National Center for Agricultural Utilization Research » Crop Bioprotection Research » Research » Publications at this Location » Publication #269485

Title: Economic Benefits of Predictive Models for Pest Control in Agricultural Crops

item Dowd, Patrick

Submitted to: Corn Dry Milling Conference Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 5/19/2011
Publication Date: 5/19/2011
Citation: Dowd, P.F. 2011. Economic benefits of predictive models for pest control in agricultural crops. Proceedings of the Corn Dry Milling Conference [abstract]. Abstract 1.

Interpretive Summary:

Technical Abstract: Various forms of crop models or decision making tools for managing crops have existed for many years. The potential advantage of all of these decision making tools is that more informed and economically improved crop management or decision making is accomplished. However, examination of some of these forms in more detail indicate that they are based on "average" situations, and individuals using the decision making strategies may find that their experiences fall well outside the average situation. The complexities of the expected decision making matrix can greatly influence it's rate of adoption. Often the involvement of an interested industry or commodity group appears to enhance getting the decision making tools into the hands of the user. Although many of these tools are based on economic considerations, there appears to be very little follow up on the continued economic benefits. For example, simple crop yield models are typically based on some form of cumulative heat units, and estimates can be far from reality because rainfall and pest pressure are not considered. Economic injury levels are often presented as a simple "treat if you see this many bugs per plant unit". There appear to be few examples where specific economic considerations, such as cost to treat, projected market value of the crop, etc. are considered. Integrated pest management can vary considerably in complexity, and rates of adoption are inversely related to the complexity. One of the most successful mycotoxin predictive models is DONcast, developed by Shaafsma et al. to forecast potential wheat scab problems; one web based version is sponsored by a fungicide company. The less complicated wheat growing environment, compared to that of corn, has helped make DONcast a resilient model. The Mycotoxin Predictive Model for Midwest Corn, developed using central Illinois commercial fields in cooperation with a grower organization, has performed well in predicting fumonisin levels in central Illinois corn fields over several years, and has also performed well in predicting averaged levels of aflatoxin in individual years. An economic decision making module is ready to be added to the Predictive Program for Mycotoxins in Corn. Future hopes are to expand the area where the program can be validated or adjusted as necessary, and to perform economic studies evaluating it's economic value alone and in conjunction with other mycotoxin management strategies, such as Bt corn.